摘要
Abstract
Chinese character skeletons are the key topological description of the Chinese character glyphs,which provides essential information about structures of the Chinese characters.The Chinese character skeleton can be regarded as a sequence of points,which can be generated by a recurrent neural network(RNN)with sequence output.However,the deep neural networks with long sequence outputs face problems such as gradient vanishing or explosion,which requires large-scale training data and long training time.This results in Chinese character skeletons generated by such methods containing fewer writing details and lacking an accurate description of the structure of Chinese characters.In the proposed method,the structural information of Chinese characters is combined with the neural network,and the Chinese character skeleton is generated by multiple parallel random recurrent networks(RRNs).The generation takes place at two levels,the stroke level includes a sequence of points,and the character level includes a sequence of strokes.The generative model can complete the training only with small-scale training data,which avoids the gradient vanishing or explosion.It not only enhances the description of the structure of Chinese characters,but also retains more skeleton features.The experimental results show that the Chinese character skeletons generated by the proposed method has richer writing details,and can be used to quickly generate large-scale and high-quality Chinese character skeletons.关键词
汉字骨架/生成模型/随机循环网络/序列生成/间架结构/分布式网络Key words
Chinese character skeleton/generative model/RRN/sequence generation/frame structure/distributed network分类
信息技术与安全科学
